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Maturity Model for Semantic Artefacts Catalogues

Core Concepts
A maturity model for assessing catalogues of semantic artefacts is presented, focusing on governance and processes to preserve and maintain these artefacts.
The content introduces a maturity model for evaluating catalogues of semantic artefacts. It analyzes 26 different catalogues across 12 dimensions, including Metadata, Openness, Quality, Availability, Statistics, PID, Governance, Community, Sustainability, Technology, Transparency, and Assessment. The model aims to provide recommendations for governance and processes to ensure the preservation and maintenance of semantic artefacts. Directory: Abstract Introduces the maturity model for assessing catalogues of semantic artefacts. Introduction Discusses the importance of data management in academia with the rise of Open Data and FAIR Principles. Definition of Semantic Artefact Explores various definitions and formalizations of semantic artifacts. Preservation Locations Describes where semantic artifacts are stored and shared using specific services. Ontology Libraries vs Registries Differentiates between ontology libraries and registries in managing ontologies. EOSC Task Forces Overview Details the four macro topics addressed by EOSC task forces related to metadata quality, research careers, technical challenges, and sustainability. Work Setting & Research Question Outlines the research question addressed by the EOSC Task Force on Semantic Interoperability. Results & Discussion Analyzes the assessment results of 26 selected catalogues against 12 dimensions related to maturity features. Methods Explains the methodology followed in identifying dimensions and analyzing catalogues.
We assessed 26 different catalogues across 12 dimensions: Metadata (Me), Openness (Op), Quality (Qu), Availability (Av), Statistics (St), PID (Pi), Governance (Go), Community (Co), Sustainability (Su), Technology (Te), Transparency (Tr), Assessment (As).
"The work presents a maturity model for assessing catalogues of semantic artefacts." "Understanding the maturity of such catalogues is crucial for envisioning how to enable long-term preservation."

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by Oscar Corcho... at 03-26-2024
A maturity model for catalogues of semantic artefacts

Deeper Inquiries

How can governance models impact the preservation of semantic artifacts?

Governance models play a crucial role in ensuring the long-term preservation of semantic artifacts. By establishing clear rules, responsibilities, and processes for managing these artifacts, governance models can provide stability and consistency in their maintenance. Here are some ways governance models impact preservation: Standardization: Governance models help standardize practices for creating, updating, and maintaining semantic artifacts. This ensures that there is uniformity in how data is managed across different systems or organizations. Quality Control: Governance frameworks often include mechanisms for quality control and validation of semantic artifacts. By setting standards for accuracy, completeness, and relevance, they ensure that only high-quality data is preserved. Sustainability: Governance structures define roles and responsibilities for sustaining the infrastructure supporting semantic artifacts. This includes securing funding, allocating resources effectively, and planning for long-term sustainability. Compliance: Governance models ensure compliance with legal requirements such as data protection regulations or intellectual property rights. By adhering to these guidelines, organizations can mitigate risks associated with non-compliance. Community Engagement: Effective governance encourages community involvement in the preservation process by providing avenues for feedback, collaboration, and contribution from stakeholders. This engagement fosters a sense of ownership among users which can lead to better artifact maintenance.

How might advancements in technology influence future developments in managing semantic interoperability?

Advancements in technology have the potential to significantly impact how we manage semantic interoperability moving forward: Automation: Technologies like artificial intelligence (AI) and machine learning (ML) can automate tasks related to mapping ontologies or aligning schemas between different systems. This automation reduces manual effort while improving accuracy. 2 .Semantic Web Standards: Continued development of Semantic Web standards like RDF (Resource Description Framework) enables more structured representation of data on the web leading to enhanced interoperability between systems. 3 .Linked Data Technologies: Linked Data principles facilitate connecting related datasets across different sources on the web using standardized formats like RDF triples linked through URIs enabling seamless integration between diverse datasets 4 .API Integration: Advancements in API technologies allow easier access to structured data making it simpler to integrate disparate systems without extensive custom development efforts 5 .Blockchain Technology: Blockchain offers secure decentralized storage solutions that could be leveraged for preserving metadata about semantic artefacts ensuring immutability 6 .Data Management Tools: Emerging tools focused on metadata management specifically tailored towards handling complex relationships within ontologies could streamline cataloging processes enhancing overall efficiency.

What are potential drawbacks or limitations in relying on open-source systems for maintaining these catalogs?

While open-source systems offer numerous benefits such as transparency flexibility cost-effectiveness there are also some drawbacks when relying solely on them: 1 .Limited Support: Open-source projects may lack dedicated customer support compared to proprietary software resulting reliance on community forums documentation which may not always address specific issues promptly 2 .Security Concerns: As open-source code is accessible publicly it may be vulnerable security threats if not regularly updated patched leaving sensitive information at risk 3 .Customization Complexity: Customizing open-source software according specific needs requires technical expertise time investment especially if modifications need made core functionalities this complexity could hinder adoption by less tech-savvy users 4 .**Integration Challenges: Integrating multiple open source tools into cohesive system pose challenges compatibility dependencies version conflicts require careful planning execution avoid disruptions workflow 5 Compatibility Issues: Compatibility issues arise when integrating various third-party plugins extensions with an opensource system These components may not always seamlessly work together causing functionality breakdowns requiring additional troubleshooting 6 Limited Features: Some opensource solutions might lack advanced features found commercial alternatives For specialized requirements organizations find themselves needing invest developing those features internally outsourcing customization costly endeavor 7 Long-Term Viability: The longevity an opensource project dependent upon active developer community ongoing contributions If key contributors lose interest move onto other projects project stagnate become obsolete over time impacting its usefulness maintainers